Over the last decade, there has been increasingly interest in applying bacterial evolutionary algorithms for solving optimization problems. The bacterial evolutionary algorithm incorporates two operators based on microbial evolution phenomenon. The bacterial mutation optimizes the bacteria individually, whilst the gene transfer allows the bacteria to directly transfer information to other bacteria in the population.

Memetic algorithms combine an evolutionary algorithm with a local search procedure in order to speed up the evolutionary process making it more efficient and find a better, more accurate solution. In this presentation these ideas are combined and the bacterial memetic algorithm is introduced. This technique applies the bacterial approach incorporating a local search procedure, which is also applied for the optimization of fuzzy rule bases.